Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
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Konstantinos Kamnitsas | Ben Glocker | Daniel Rueckert | Bernhard Kainz | Wenjia Bai | Ozan Oktay | Enzo Ferrante | Antonio de Marvao | Jose Caballero | Mattias Heinrich | Declan O'Regan | Stuart Cook | Timothy Dawes | D. Rueckert | Jose Caballero | M. Heinrich | Ben Glocker | K. Kamnitsas | D. O’Regan | O. Oktay | Wenjia Bai | S. Cook | A. de Marvao | Enzo Ferrante | T. Dawes | Bernhard Kainz
[1] Phi Vu Tran,et al. A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI , 2016, ArXiv.
[2] Hamid Jafarkhani,et al. Automatic segmentation of the right ventricle from cardiac MRI using a learning‐based approach , 2017, Magnetic resonance in medicine.
[3] Jianxiong Xiao,et al. 3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Christian Ledig,et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Camille Couprie,et al. Semantic Segmentation using Adversarial Networks , 2016, NIPS 2016.
[6] Daniel Rueckert,et al. A Probabilistic Patch-Based Label Fusion Model for Multi-Atlas Segmentation With Registration Refinement: Application to Cardiac MR Images , 2013, IEEE Transactions on Medical Imaging.
[7] Daniel Rueckert,et al. Population-based studies of myocardial hypertrophy: high resolution cardiovascular magnetic resonance atlases improve statistical power , 2014, Journal of Cardiovascular Magnetic Resonance.
[8] Hayit Greenspan,et al. Super-Resolution in Medical Imaging , 2009, Comput. J..
[9] Oliver Grau,et al. VConv-DAE: Deep Volumetric Shape Learning Without Object Labels , 2016, ECCV Workshops.
[10] Dinggang Shen,et al. Hierarchical active shape models, using the wavelet transform , 2003, IEEE Transactions on Medical Imaging.
[11] Denis Friboulet,et al. Fast and fully automatic 3-d echocardiographic segmentation using B-spline explicit active surfaces: feasibility study and validation in a clinical setting. , 2013, Ultrasound in medicine & biology.
[12] Hao Chen,et al. Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images , 2016, MICCAI.
[13] Yoshua Bengio,et al. What regularized auto-encoders learn from the data-generating distribution , 2012, J. Mach. Learn. Res..
[14] Hao Chen,et al. DCAN: Deep contour‐aware networks for object instance segmentation from histology images , 2017, Medical Image Anal..
[15] Zhuowen Tu,et al. Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] Ben Glocker,et al. Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images , 2017, IEEE Transactions on Medical Imaging.
[17] Daniel Rueckert,et al. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[19] Li Fei-Fei,et al. Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.
[20] Ghassan Hamarneh,et al. Topology Aware Fully Convolutional Networks for Histology Gland Segmentation , 2016, MICCAI.
[21] Samuel Kadoury,et al. Deep Spectral-Based Shape Features for Alzheimer's Disease Classification , 2016, SeSAMI@MICCAI.
[22] Huimin Yu,et al. Deep Learning Shape Priors for Object Segmentation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[23] Jürgen Schmidhuber,et al. Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction , 2011, ICANN.
[24] Christopher K. I. Williams,et al. A Generative Model for Parts-based Object Segmentation , 2012, NIPS.
[25] Timothy F. Cootes,et al. Combining point distribution models with shape models based on finite element analysis , 1994, Image Vis. Comput..
[26] P. Matthews,et al. UK Biobank’s cardiovascular magnetic resonance protocol , 2015, Journal of Cardiovascular Magnetic Resonance.
[27] Hariharan Ravishankar,et al. Joint Deep Learning of Foreground, Background and Shape for Robust Contextual Segmentation , 2017, IPMI.
[28] Xiaoou Tang,et al. Accelerating the Super-Resolution Convolutional Neural Network , 2016, ECCV.
[29] Christopher K. I. Williams,et al. The Shape Boltzmann Machine: A Strong Model of Object Shape , 2012, International Journal of Computer Vision.
[30] Konstantinos Kamnitsas,et al. Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..
[31] Abhinav Gupta,et al. Learning a Predictable and Generative Vector Representation for Objects , 2016, ECCV.
[32] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.
[33] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[34] Hariharan Ravishankar,et al. Learning and Incorporating Shape Models for Semantic Segmentation , 2017, MICCAI.
[35] Jitendra Malik,et al. Iterative Instance Segmentation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Otto Kamp,et al. EAE/ASE recommendations for image acquisition and display using three-dimensional echocardiography. , 2012, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.
[37] Eero P. Simoncelli,et al. Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.
[38] Konstantinos Kamnitsas,et al. Multi-input Cardiac Image Super-Resolution Using Convolutional Neural Networks , 2016, MICCAI.
[39] Antonio Torralba,et al. Small codes and large image databases for recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[40] Ghassan Hamarneh,et al. Incorporating prior knowledge in medical image segmentation: a survey , 2016, ArXiv.
[41] Chen Wang,et al. Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography , 2016, IEEE Transactions on Medical Imaging.
[42] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[43] Hamid Jafarkhani,et al. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI , 2015, Medical Image Anal..
[44] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[45] Graham W. Taylor,et al. Deconvolutional networks , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[46] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[47] Thomas Brox,et al. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.
[48] L. Anderson,et al. The Role of Cardiovascular Magnetic Resonance Imaging in Heart Failure. , 2016, Cardiac failure review.
[49] Daniel Rueckert,et al. A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion , 2015, Medical Image Anal..